https://education.sdsc.edu/studenttech/map-home/
Open the terminal and add a line (if missing) to .profile in the home directory. This automatically loads the .bashrc file in future terminals.
source ~/.bashrc
Initialize conda environment. This adds a script to .bashrc to configure Conda.
mamba init
Restart the terminal or run source ~/.bashrc.
Activate the label-studio environment and start Label Studio. You should see (label-studio) in the command prompt.
mamba activate /opt/conda/envs/label-studio
export LABEL_STUDIO_HOST=https://datahub.ucsd.edu/user/$USER/proxy/8080
export CSRF_TRUSTED_ORIGINS=https://datahub.ucsd.edu
label-studio start
Access Label Studio at http://datahub.ucsd.edu/hub/user-redirect/proxy/8080.
These commands will reset the environment.
rm -rf ~/.local/share/label-studio/
rm -rf ~/.config/label-studio
Select a notebook with a GPU to use CUDA hardware acceleration.
Within the notebook, select the label-studio kernel.
Sample code
from ultralytics import YOLO
# Create a new YOLO model from scratch
model = YOLO("yolo26n.yaml")
# Load a pretrained YOLO model (recommended for training)
model = YOLO("yolo26n.pt")
# Train the model using the 'coco8.yaml' dataset for 3 epochs
results = model.train(data="coco8.yaml", epochs=3)
# Evaluate the model's performance on the validation set
results = model.val()
# Perform object detection on an image using the model
results = model("https://ultralytics.com/images/bus.jpg")
# Export the model to ONNX format
success = model.export(format="onnx")